The leading agentic risk platform for fighting financial crime. Our integrated solution unifies data across risk teams to help organizations stop fraud in real time, prevent AI-driven attacks, and automate fraud and AML operations. The platform is strengthened by one of the fastest-growing fraud consortiums in the market, spanning more than 6 billion profiled devices, 800 million consumers, and 3 million businesses worldwide. Leading companies including FIS, GoDaddy, Intuit, Edward Jones, ZoomInfo, and Checkout.com rely on us to secure and grow trust in their products.
Our culture:
- We have hubs in the Bay Area, NYC, Austin, Toronto, and São Paulo. However, we maintain a remote-first work culture. #WorkFromAnywhere
- We hire talented, self-motivated individuals with extreme ownership and high growth orientation.
- We value performance and not hours worked. We believe you shouldn't have to miss your family dinner, your kid's school play, friends get-together, or doctor's appointments for the sake of adhering to an arbitrary work schedule.
Location:
- Remote - United States or Canada
- From Home / Beach / Mountain / Cafe / Anywhere!
- We are a remote-first company with a globally distributed team. You can find your productive zone and work from there.
Compensation
- US: Estimated base salary $175K – $220K • Offers Equity
- Canada: Estimated base salary CA$210K – CA$265K • Offers Equity
About The Role
As a Machine Learning Engineer, you’ll do more than build models - you’ll design the systems that make fraud detection possible. You’ll work across modeling, data pipelines, and backend systems (Go) to ensure ML models run reliably, efficiently, and at scale.
This is a chance to combine applied ML with large-scale systems engineering, owning end-to-end solutions that tackle high-stakes, ever-evolving challenges.
What you’ll be doing:
- Build and optimize data pipelines and backend services to process device and behavioral data in real time.
- Develop and deploy ML models for fraud detection, ensuring they run reliably and efficiently in production.
- Turn raw data into production-ready features that feed our fraud detection systems.
- Collaborate with platform and backend engineers to integrate models seamlessly.
- Maintain high standards of security, privacy, and compliance.
- Champion best practices in testing, documentation, and observability.
What you’ll need:
- 5+ years in software engineering, with strong backend experience (Go or Python).
- Hands-on experience with applied ML using large datasets (PyTorch, Scikit-learn, etc.).
- Strong SQL skills and familiarity with relational and non-relational databases.
- Experience with end-to-end ML systems: feature pipelines, model deployment, monitoring, and iteration.
- Excellent communication skills in English, both written and verbal.
- Bachelor's or Master's in Computer Science, Engineering, or a related discipline.
Bonus Points
- Domain knowledge in fraud, risk, or cybersecurity.
- Familiarity with CI/CD, Docker, Kubernetes and the modern devops framework.
- Understanding of modern browser APIs and high-entropy data collection techniques.
- Familiarity with leveraging frontier LLMs for automation.
Benefits we offer:
- Generous compensation in cash and equity
- Early exercise for all options, including pre-vested
- Work from anywhere: Remote-first Culture
- Flexible paid time off and Year-end break
- Health insurance, dental, and vision coverage for employees and dependents - US and Canada specific
- 4% matching in 401k / RRSP - US and Canada specific
- MacBook Pro delivered to your door
- One-time stipend to set up a home office — desk, chair, screen, etc.
- Monthly meal stipend
- Monthly social meet-up stipend
- Annual health and wellness stipend
- Annual Learning stipend